CN113079262A

CN113079262A - Data processing method and device for intelligent voice conversation, electronic equipment and medium

Info

Publication number: CN113079262A
Application number: CN202110315549.XA
Authority: CN
Inventors: 金晓波; 杨帆; 黄庆伟; 林晓斌
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-03-24
Filing date: 2021-03-24
Publication date: 2021-07-06
Anticipated expiration: 2041-03-24
Also published as: CN113079262B

Abstract

The present disclosure provides a data processing method and apparatus for intelligent voice dialog, an electronic device, a computer-readable storage medium, and a computer program product, which relate to the field of artificial intelligence, and in particular, to the field of intelligent voice dialog. The data processing method comprises the following steps: obtaining configuration information associated with an intelligent voice conversation application and call information associated with an intelligent voice conversation; classifying the configuration information and the call information according to a first preset rule; determining the number of configuration information and the number of call information corresponding to each classification; and updating the intelligent voice dialog application based on the determined number of configuration information and the determined number of call information corresponding to each category.

Description

Data processing method and device for intelligent voice conversation, electronic equipment and medium

Technical Field

The present disclosure relates to the field of artificial intelligence, and in particular, to the field of intelligent voice dialogs, and more particularly, to a data processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product for intelligent voice dialogs.

Background

With the rapid development of computers and artificial intelligence technologies, intelligent voice dialogs are widely developed and used, and intelligent voice dialog applications have increasingly gone into people's lives and works. Under the scenes that a user cannot be connected, busy, refused to answer and the like, the intelligent voice conversation application is executed, so that multiple rounds of intelligent conversation with a calling call can be realized or the calling call can be automatically hung up, the user is helped to answer strange and missed calls, and the user is helped to intercept harassing calls.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.

Disclosure of Invention

According to a first aspect of the present disclosure, there is provided a data processing method for intelligent voice dialog, comprising: obtaining configuration information associated with an intelligent voice conversation application and call information associated with an intelligent voice conversation; classifying the configuration information and the call information according to a first preset rule; determining the number of configuration information and the number of call information corresponding to each classification; and updating the intelligent voice dialog application based on the determined number of configuration information and the determined number of call information corresponding to each category.

According to a second aspect of the present disclosure, there is provided a data processing apparatus for intelligent voice dialog, comprising: a first obtaining module configured to obtain configuration information associated with an intelligent voice conversation application and call information associated with an intelligent voice conversation; the first classification module is configured to classify the configuration information and the call information according to a first preset rule; a first determination module configured to determine the number of configuration information and the number of call information corresponding to each classification; and a first updating module configured to update the intelligent voice dialog application based on the determined number of configuration information and the determined number of call information corresponding to each category.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: a memory, a processor and a computer program stored on the memory, wherein the processor is configured to execute the computer program to implement the steps of the data processing method of the intelligent voice dialog.

According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when being executed by a processor, is for implementing the steps of the data processing method of a smart voice dialog.

According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the data processing method of a smart voice dialog.

According to one or more embodiments of the present disclosure, data information associated with an intelligent voice conversation can be grasped in multiple dimensions, facilitating efficient quantitative statistical analysis.

According to one or more embodiments of the present disclosure, conversation efficiency of smart voice conversation may be improved.

According to one or more embodiments of the present disclosure, a smart voice dialog application may be continuously optimized, enhancing user operability.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 shows a flow diagram of a data processing method for intelligent voice dialogs, according to an embodiment of the present disclosure;

fig. 2 illustrates a flowchart of classifying call information according to a first preset rule and determining the number of call information corresponding to each classification according to an embodiment of the present disclosure;

fig. 3 illustrates a flowchart of classifying call information according to a first preset rule and determining the number of call information corresponding to each classification according to another embodiment of the present disclosure;

FIG. 4 illustrates a flow diagram for updating an intelligent voice dialog application based on a determined amount of configuration information and an amount of call information corresponding to each category in accordance with an embodiment of the present disclosure;

FIG. 5 shows a flow diagram of a data processing method for intelligent voice dialogs, according to another embodiment of the present disclosure;

FIG. 6 shows a block diagram of a data processing apparatus for intelligent voice dialog, in accordance with an embodiment of the present disclosure;

FIG. 7 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, in accordance with embodiments of the present disclosure;

FIG. 8 illustrates a block diagram of an exemplary server and client that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

The inventor finds that a large amount of data is generated in the process of executing the intelligent voice conversation application, but the related art lacks multidimensional statistics and processing on the data, so that the intelligent voice conversation development platform has difficulty in mastering data processing results more comprehensively and intuitively and analyzing the value of the data. This will further lead to the inability of the intelligent voice dialog development platform to specifically optimize and update the intelligent voice dialog application, and to provide the user with a more efficient and operational intelligent voice dialog application.

In view of the above technical problems, one or more embodiments of the present disclosure provide a data processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product for intelligent voice dialog. Various embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and do not violate the good customs of the public order.

Fig. 1 shows a flow diagram of a data processing method 100 for intelligent voice dialogs, according to an embodiment of the present disclosure. As shown in fig. 1, the data processing method 100 may include: step S110, acquiring configuration information associated with the intelligent voice conversation application and call information associated with the intelligent voice conversation; step S120, classifying the configuration information and the call information according to a first preset rule; step S130, determining the number of configuration information and the number of call information corresponding to each classification; and step S140, updating the intelligent voice conversation application based on the determined number of the configuration information and the determined number of the call information corresponding to each classification.

According to the data processing method for the intelligent voice conversation, the data information associated with the intelligent voice conversation can be counted and processed in a multi-dimensional mode, so that the data processing result can be mastered more comprehensively and intuitively, and the value of the data can be analyzed.

Further, the data processing method can continuously optimize the intelligent voice conversation application based on the processed data information, improve the conversation efficiency of the intelligent voice conversation and enhance the operability of the user. According to some embodiments, the classifying the configuration information according to the first preset rule in step S120 may include classifying the configuration information according to at least one of: tone information, open scene information, scene reply information, disappearing scene information, and refusing to answer the scene information.

According to some examples, the timbre information may refer to a timbre classification presented during the smart voice conversation, such as timbre 1-vibrant female voice, timbre 2-deep male voice, timbre 3-dialect of places, and so forth. The opening white information may refer to a greeting presented when the smart voice conversation is switched on, for example, "do you get, your phone is busy, ask what can be busy you? "," hello, you make a call that is temporarily unavailable and please call later, "etc. The scene reply information may refer to an auto reply language set for at least one scene in the preset scene classification set in the intelligent voice conversation process, for example, "i arrive at the boarding place immediately and please wait a little" for the taxi taking scene auto reply, "please put take out to the meal cabinet" for the take-out scene auto reply, "please put express to the express cabinet" for the express scene auto reply, and the like. The rejectional scenario information may refer to a scenario classification of replying a rejectional language during an intelligent voice conversation, such as a house property intermediary scenario, a buy-sell second-hand item scenario, and the like. The answering refusal scene information can refer to scene classification for directly refusing answering in the intelligent voice conversation process, such as adding friend scenes and the like.

According to some embodiments, after classifying the configuration information according to at least one of tone color information, open scene information, scene reply information, rejecture scene information, and refusal to answer scene information, the number of users, usage distribution, etc. of the configuration information corresponding to each classification may be determined. For example, in the case where the total number of acquired configuration information is 100, if the number of configuration information corresponding to vital female voices in the tone color classification is 40, the usage distribution corresponding to the tone color 1-vital female voice classification is 40%.

The configuration information is classified according to at least one of tone information, field opening information, scene reply information, disappearing scene information and answer refusing scene information, the use condition of the user can be comprehensively and intuitively mastered, and therefore the intelligent voice conversation application can be optimized based on the preference degree of the user.

It should be understood that the first preset rule for classifying the configuration information is not limited to the above listed tone color information, the open field white information, the scene reply information, the rejectable scene information, and the refusal to answer scene information, and any suitable preset rule can be adopted to classify the configuration information according to the scene requirements.

Fig. 2 illustrates a flowchart of classifying call information according to a first preset rule and determining the number of call information corresponding to each classification according to an embodiment of the present disclosure. As shown in fig. 2, the classifying the call information according to the first preset rule in step S120 may include: step S222, extracting at least one key word associated with the call information; step S224, determining whether at least one keyword word is matched with at least one scene classification in a preset scene classification set; and step S226, in response to determining that the at least one keyword matches at least one scene classification in the preset scene classification set, determining at least one scene classification corresponding to the call information as matching classification.

According to some embodiments, extracting the keyword associated with the call information may employ various algorithms, including but not limited to: automatic Speech Recognition (ASR) based keyword extraction algorithms, Natural Language Processing (NLP) based keyword extraction algorithms, and subject model based keyword extraction algorithms. In one example, speech recognition may be performed on the call information by ASR to obtain a recognition result in the form of text. For example, the recognized text-form call message is "you take away, please take a meal down", and the key words such as "take away", "take down", and "take a meal" can be obtained based on the above-described key word extraction algorithm. In order to match the extracted key words, a scene classification set needs to be preset. The preset scene classification set may include common scenes involved in the intelligent voice call, and may also be scenes associated with configuration information, such as express delivery, takeaway, intermediation, taxi taking, friend adding, and the like. Continuing with the above example, since "take out" and "fetch" in the extracted keyword words match "take out" in the preset scene classification set, the classification corresponding to the call information may be determined to be "take out".

It should be noted that the matching here is not limited to that the extracted keyword must be completely consistent with the scene classification in the preset scene classification set, as long as the extracted keyword is associated with the scene classification. For example, "meal-taking" in the above example may not be completely consistent with any one of the scene classifications in the preset set of scene classifications, but since it is associated with "take-out" in the preset set of scene classifications, the two may be considered to be matched, so that the classification of the call information associated with "meal-taking" may be determined as a "take-out" scene.

It should also be noted that the first preset rule in this document may be a set of rules. The set may include a subset of rules for classifying configuration information and classifying call information, respectively.

Extracting the key words associated with the call information through the algorithms of automatic speech recognition, natural language processing and the like as described above can more quickly and accurately match the call information with the scene classification in the preset scene classification set. Further, based on fast and accurate scene matching, the accuracy of the obtained multi-dimensional data information can be improved.

Continuing with the above embodiment, determining the amount of call information corresponding to each category in step S130 may include: step S232, increment the number of call information in the at least one matched scene classification by 1.

Alternatively or additionally, at least one of a scene completion amount and a scene completion rate corresponding to the matched scene may be determined in response to determining whether the smart voice dialog in the matched scene has been completed, wherein the scene completion amount may represent the number of call information for which the smart voice dialog in the matched scene has been completed, and the scene completion rate may represent a ratio of the scene completion amount and the total number of call information matched with the scene.

According to some examples, determining whether a smart voice conversation under a matching scenario has been completed may include: presetting an intelligent voice conversation turn number threshold or an intelligent voice conversation end node corresponding to each scene; determining whether a round of intelligent voice conversation with a calling number is completed; responding to the determination of completing one round of intelligent voice conversation with the calling number, increasing the number of rounds of intelligent voice conversation by 1 or determining that the intelligent voice conversation completes the current node; in response to determining that the number of intelligent voice conversation turns reaches an intelligent voice conversation turn threshold or an intelligent voice conversation completion end node, determining that intelligent voice conversation under a matched scene is completed; and determining that the intelligent voice conversation is not completed under the matched scene in response to determining that the intelligent voice conversation is hung up before the intelligent voice conversation round number threshold value is not reached or the intelligent voice conversation is not completed before the end node.

According to some examples, it is determined that a round of intelligent voice conversations in the call information is not completed if the extracted keyword word associated with the round of intelligent voice conversations matches another scene classification in the preset set of scene classifications.

By determining at least one of the scene completion amount and the scene completion rate corresponding to the matched scene, the accuracy of classifying the call information can be more accurately grasped, and thus the accuracy of classifying the call information can be further improved by optimizing a keyword extraction and matching algorithm.

Fig. 3 illustrates a flowchart of classifying call information according to a first preset rule and determining the number of call information corresponding to each classification according to another embodiment of the present disclosure. As shown in fig. 3, the classifying the call information according to the first preset rule in step S120 may include: step S322, in response to determining that the call information indicates to refuse answering, obtaining a call number associated with the call information; step S324 of determining whether the call number is included in a predetermined group of numbers; step S326, in response to determining that the call number is included in the predetermined set of numbers, determining that the classification corresponding to the call information is a first answer rejection classification; and step S328, in response to determining that the call number is not included in the predetermined set of numbers, determining that the classification corresponding to the call information is a second answer rejection classification. With continued reference to fig. 3, the determining of the number of call information corresponding to each classification in step S130 includes: step S332, in response to determining that the call number is included in a predetermined group of numbers, incrementing the number of call information in the first refusal answering category by 1; and step S334 of incrementing the number of call information in the second reject answer category by 1 in response to determining that the call number is not included in the predetermined set of numbers.

According to some embodiments, the predetermined set of numbers may be pre-identified and stored nuisance telephone numbers such as financial fraud, telephone sales, black lists set by users, and the like. If the call information indicates to reject answering, if the call number is determined to match one of the predetermined group of numbers, determining that the classification corresponding to the call information is a first answering rejection classification, which may include, but is not limited to, a number marking answering rejection classification, and a blacklisting answering rejection classification. If the call number is determined not to be matched with any number in the predetermined group of numbers, determining that the classification corresponding to the call information is a second answer rejection classification, which may include, but is not limited to, an intelligent voice conversation application deactivation classification, an intelligent voice conversation application unopened classification, and the like.

By classifying and counting the call information refused to answer and updating the intelligent voice conversation application according to the counting result, the identified and stored harassing call numbers can be continuously expanded and updated, so that the intelligent voice conversation application is prevented from executing unnecessary conversations, the working efficiency of the intelligent voice conversation application is improved, and the probability that the user is harassed by the harassing call numbers is further reduced.

According to further embodiments, after step S334, at least one of the following may be further determined: the call information switching method comprises the steps of indicating the number of the call information which is switched on, indicating the ratio of the number of the call information which is switched on to the total number of the call information, and indicating the call time length of the call information which is switched on. By determining the data information, for example, in the case that the call duration of the call information indicating the call is short, the response strategy associated with the intelligent voice conversation can be optimized, the conversation quality can be improved, on one hand, more comprehensive information about the user can be provided for the calling number, and on the other hand, the optimized intelligent voice conversation is beneficial to avoiding the user missing important calls.

According to other embodiments, after step S334, a periodic data report, such as a daily report, a weekly report, a monthly report, etc., of the call information may be formed based on the determined amount of the call information, and the periodic data report may be pushed to at least one of the intelligent voice dialog development platform or the user, so as to facilitate the intelligent voice dialog development platform or the user to grasp the usage of the intelligent voice dialog application in time.

Fig. 4 illustrates a flow diagram for updating an intelligent voice dialog application based on the determined amount of configuration information and the determined amount of call information corresponding to each category in accordance with an embodiment of the present disclosure. As shown in fig. 4, step S140 may include: a step S442 of updating the configuration information for display based on at least one of the determined number of configuration information and the determined number of call information corresponding to each category; step S444 of updating a reply policy associated with the intelligent voice conversation based on the determined at least one of the number of configuration information and the number of call information corresponding to each category; and step S446 updates the first preset rule based on at least one of the determined number of configuration information and the determined number of call information corresponding to each classification.

Step S442, step S444, and step S446 may be executed in parallel, or may be executed in a different order from that shown in fig. 4. Furthermore, fig. 4 only shows one embodiment of step S140, and according to some other embodiments, step S140 may include one or more of step S442, step S444, and step S446, which is not limited herein. By updating the configuration information for display, a more convenient user interface may be provided, facilitating recommendation of more popular selections for the user. For example, a leaderboard of configuration information may be formed based on the determined number of users, distribution of use, etc. of configuration information corresponding to each category as described above. For another example, personalized recommendations for the users may be formed based on the obtained configuration information corresponding to each user.

By updating the response strategy associated with the intelligent voice conversation, the conversation quality under different scene requirements can be improved, the intelligent voice conversation effect is optimized, and the loss caused by missing important calls can be reduced. For example, whether the response policy in different scenes needs to be optimized or not can be grasped and analyzed based on the determined scene completion amount, the scene completion rate, and the like as described above, and different optimization policies for different scenes can be implemented.

By updating the first preset rule, the classification of the configuration information and the call information can be more detailed and comprehensive, and the classification accuracy can be improved.

Fig. 5 shows a flow diagram of a data processing method for intelligent voice dialogs according to another embodiment of the present disclosure. As shown in fig. 5, method 500 may include: steps S510-S540, which are the same as or similar to the embodiments of steps S110-S140 in fig. 1, and step S532, obtaining time information associated with accessing the intelligent voice dialog application; step S534, classifying the time information according to the second preset rule; and a step S536 of determining the number of the time information corresponding to each classification.

According to some embodiments, in step S532, the time information may include a call duration of the intelligent voice conversation, a duration of time the user accessed the intelligent voice conversation application, a time of day, and the like.

Continuing with the above embodiment, in step S534, the second preset rule may include at least one of a usage duration threshold and a usage frequency threshold. For example, the duration of use threshold may be any suitable time, such as 1h, 5h, 10h, etc., and the classification of the time information as user-sticky may be determined if the duration of the call of the smart voice conversation or the duration of the user's access to the smart voice conversation application exceeds the duration of use threshold. As another example, the usage frequency threshold may be any suitable time period, such as 1 time a day, 1 time a week, 1 time a month, etc., and the classification of the time information as being highly active may be determined if the usage frequency threshold is determined to be exceeded based on the time of the user's access to the smart voice dialog application.

Continuing with the above embodiment, in step S536, based on the classification of the time information, the amount of time information classified as high user stickiness or high user activity may be determined, for example. Further, for example, the number of active users, the retention rate of N-day users, the loss rate of N-day users, and the like can be determined.

By classifying the time information and determining the amount of time information corresponding to each classification, the intelligent voice conversation development platform can know the use condition of the intelligent voice conversation application, for example, the intelligent voice conversation application can be optimized and updated in time particularly under the conditions of low user activity and low N-day user retention rate.

It should be noted that steps S532 to S536 may be executed in parallel with steps S510 to S540, or may be executed in a sequence different from that shown in fig. 5, as long as the above-mentioned data processing method for intelligent voice dialogue can be implemented, and the present disclosure is not limited thereto.

Fig. 6 shows a block diagram of a data processing apparatus 600 for intelligent voice dialog according to an embodiment of the present disclosure.

According to some embodiments, as shown in fig. 6, the data processing apparatus 600 may include: a first obtaining module 610 configured to obtain configuration information associated with the intelligent voice dialog application and call information associated with the intelligent voice dialog; a first classification module 620 configured to classify the configuration information and the call information according to a first preset rule; a first determining module 630 configured to determine the number of configuration information and the number of call information corresponding to each classification; and an update module 640 configured to update the intelligent voice dialog application based on the determined number of configuration information and the determined number of call information corresponding to each category.

According to some embodiments, classifying the configuration information according to the first preset rule may include classifying the configuration information according to at least one of: tone information, open scene information, scene reply information, disappearing scene information, and refusing to answer the scene information.

According to some embodiments, the first classification module 620 may include: a module configured to extract at least one keyword associated with the call information; a module configured to determine whether the at least one keyword word matches at least one scene classification in a preset set of scene classifications; and a module configured to determine at least one scene classification corresponding to the call information as matching in response to determining that the at least one keyword word matches at least one scene classification in the preset set of scene classifications; and the first determining module 630 includes: a module configured to increment the number of call information in the at least one scene classification that matches by 1.

According to some embodiments, the first classification module 620 may include: a module configured to obtain a call number associated with the call information in response to determining that the call information indicates a rejection to answer; a module configured to determine whether a call number is included in a predetermined set of numbers; a module configured to determine a classification corresponding to the call information as a first decline to answer classification in response to determining that the call number is included in a predetermined set of numbers; and a module configured to determine a classification corresponding to the call information as a second reject answer classification in response to determining that the call number is not included in the predetermined set of numbers; and the first determining module 630 includes: a module configured to increment a number of call information in a first decline answer category by 1 in response to determining that the call number is included in a predetermined set of numbers; and incrementing the number of call messages in the second reject answer category by 1 in response to determining that the call number is not included in the predetermined set of numbers.

According to some embodiments, the update module 640 may include at least one of: a module configured to update the configuration information for display based on the determined at least one of the number of configuration information and the number of call information corresponding to each category; a module configured to update a reply policy associated with the intelligent voice conversation based on the determined at least one of the number of configuration information and the number of call information corresponding to each category; and a module configured to update the first preset rule based on at least one of the determined number of configuration information and the determined number of call information corresponding to each classification.

According to some embodiments, the data processing apparatus 600 may further comprise: a second acquisition module configured to acquire time information associated with accessing the intelligent voice dialog application; the second classification module is configured to classify the time information according to a second preset rule; and a second determination module configured to determine an amount of time information corresponding to each classification.

According to some embodiments, the second preset rule may include at least one of: a usage duration threshold and a usage frequency threshold.

In the above embodiments, reference may be made to the various embodiments described with reference to fig. 1 to 5 for specific implementation and technical effects of the apparatus 600 and the corresponding functional modules thereof, which are not described herein again.

Referring now to fig. 7, a schematic diagram of an exemplary system 700 in which the various methods and apparatus described herein may be implemented is depicted. System 700 can be configured in accordance with any of the data processing methods described above (e.g., data processing method 100, data processing method 500) to enable processing of data associated with intelligent voice conversation.

As shown in fig. 7, system 700 includes one or

more client devices

701, 702, 703, 704, 705, and 706, a server 720, and one or more communication networks 710 coupling the one or more client devices to server 720.

Client devices

701, 702, 703, 704, 705, and 706 may be configured to execute one or more application programs.

In embodiments of the present disclosure, server 720 may run one or more services or software applications that enable execution of data processing methods for intelligent voice conversations.

In some embodiments, server 720 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, such as provided to users of

client devices

701, 702, 703, 704, 705, and/or 706 under a software as a service (SaaS) model.

In the configuration shown in fig. 7, server 720 may include one or more components that implement the functions performed by server 720. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user

operating client devices

701, 702, 703, 704, 705, and/or 706 may, in turn, utilize one or more client applications to interact with server 720 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 700. Accordingly, fig. 7 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

Client devices

701, 702, 703, 704, 705, and/or 706 may provide interfaces that enable a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 7 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.

Client devices

701, 702, 703, 704, 705, and/or 706 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and so forth. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (e.g., Google Chrome OS); or include various Mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular telephones, smart phones, tablets, Personal Digital Assistants (PDAs), and the like. Wearable devices may include head mounted displays and other devices. The gaming system may include a variety of handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), Short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 710 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 710 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.

Server 720 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. Server 720 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, server 720 may run one or more services or software applications that provide the functionality described above.

The computing units in server 720 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 720 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some implementations, the server 720 may include one or more applications to analyze and merge data feeds and/or event updates received from users of the

client devices

701, 702, 703, 704, 705, and 706. Server 720 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of

client devices

701, 702, 703, 704, 705, and 706.

In some embodiments, server 720 may be a server of a distributed system, or a server that incorporates a blockchain. The server 720 may also be a cloud server, or an intelligent cloud computing server or an intelligent cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the traditional physical host and Virtual Private Server (VPS) service.

The system 700 may also include one or more databases 730. In some embodiments, these databases may be used to store data and other information. For example, one or more of databases 730 may be used to store information such as configuration information, call information, a set of scene classifications, and the like. The data store 730 may reside in various locations. For example, a data store used by server 720 may be local to server 720, or may be remote from server 720 and in communication with server 720 via a network-based or dedicated connection. The data store 730 may be of different types. In certain embodiments, the data store used by server 720 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.

In some embodiments, one or more of databases 730 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.

According to another aspect of the present disclosure, there is also provided an electronic device including: a memory, a processor and a computer program stored on the memory, wherein the processor is configured to execute the computer program to implement the steps of the data processing method for intelligent voice dialog described above.

According to yet another aspect of the present disclosure, there is also provided a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described data processing method for intelligent voice dialog.

According to yet another aspect of the present disclosure, there is also provided a computer program product comprising a computer program, wherein the computer program realizes the steps of the above data processing method for intelligent voice dialogue when executed by a processor.

Referring to fig. 8, a block diagram of a structure of an electronic device 800, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 may be any type of device capable of inputting information to the device 800, and the input unit 806 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 807 can be any type of device capable of presenting information and can include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 808 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 1302.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.

Computing unit 801 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 801 executes the respective methods and processes described above, such as the data processing method 100, the data processing method 500. For example, in some embodiments, the data processing method 100, the data processing method 500 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM 802 and/or communications unit 809. When loaded into RAM 803 and executed by the computing unit 801, a computer program may perform one or more of the steps of the data processing method for intelligent voice dialog described above. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the above-described data processing method for intelligent voice dialog by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims

1. A data processing method for intelligent voice conversations, comprising:

obtaining configuration information associated with an intelligent voice conversation application and call information associated with the intelligent voice conversation;

classifying the configuration information and the call information according to a first preset rule;

determining the number of the configuration information and the number of the call information corresponding to each classification; and

updating the intelligent voice dialog application based on the determined quantity of the configuration information and the quantity of the call information corresponding to each classification.

2. The data processing method of claim 1, wherein classifying the configuration information according to the first preset rule comprises classifying the configuration information according to at least one of: tone information, open scene information, scene reply information, disappearing scene information, and refusing to answer the scene information.

3. The data processing method according to claim 1, wherein classifying the call information according to the first preset rule, and determining the number of call information corresponding to each classification comprises:

extracting at least one keyword associated with the call information;

determining whether the at least one keyword word matches at least one scene classification in a preset scene classification set;

in response to determining that the at least one keyword word matches at least one scene classification in a preset scene classification set, determining at least one scene classification corresponding to the call information as matching; and

and increasing the number of the call information in the matched at least one scene classification by 1.

4. The data processing method according to claim 1, wherein the call information is classified according to the first preset rule, and determining the number of call information corresponding to each classification includes:

in response to determining that the call information indicates a rejection to answer, obtaining a call number associated with the call information;

determining whether the call number is included in a predetermined set of numbers;

in response to determining that the call number is included in the predetermined set of numbers:

determining that the classification corresponding to the call information is a first answer rejection classification, and increasing the number of the call information in the first answer rejection classification by 1; and

in response to determining that the call number is not included in the predetermined set of numbers:

determining that the classification corresponding to the call information is a second answer rejection classification, and incrementing the number of the call information in the second answer rejection classification by 1.

5. The data processing method of any of claims 1-4, wherein updating the intelligent voice dialog application based on the determined quantity of configuration information and quantity of call information corresponding to each category comprises at least one of:

updating the configuration information for display based on the determined at least one of the number of configuration information and the number of call information corresponding to each category;

updating a reply policy associated with the intelligent voice conversation based on the determined at least one of the amount of configuration information and the amount of call information corresponding to each category; and

updating the first preset rule based on at least one of the determined number of the configuration information and the number of the call information corresponding to each classification.

6. The data processing method of any of claims 1-4, further comprising:

obtaining time information associated with accessing the intelligent voice dialog application;

classifying the time information according to a second preset rule; and

determining the amount of the time information corresponding to each classification.

7. The data processing method of claim 6, wherein the second preset rule comprises at least one of: a usage duration threshold and a usage frequency threshold.

8. A data processing apparatus for intelligent voice dialog, comprising:

a first obtaining module configured to obtain configuration information associated with a smart voice dialog application and call information associated with the smart voice dialog;

the first classification module is configured to classify the configuration information and the call information according to a first preset rule;

a first determination module configured to determine the number of the configuration information and the number of the call information corresponding to each classification; and

an update module configured to update the intelligent voice dialog application based on the determined quantity of the configuration information and the quantity of the call information corresponding to each category.

9. The data processing apparatus according to claim 8, wherein classifying the configuration information according to the first preset rule comprises classifying the configuration information according to at least one of: tone information, open scene information, scene reply information, disappearing scene information, and refusing to answer the scene information.

10. The data processing apparatus of claim 8,

the first classification module comprises:

a module configured to extract at least one keyword associated with the call information;

a module configured to determine whether the at least one keyword word matches at least one scene classification in a preset set of scene classifications; and

means configured to determine at least one scene classification corresponding to the call information as matching in response to determining that the at least one keyword word matches at least one scene classification in a preset set of scene classifications; and is

The first determining module includes:

a module configured to increment the number of call information in the matched at least one scene classification by 1.

11. The data processing apparatus of claim 8,

the first classification module comprises:

means configured to obtain a call number associated with the call information in response to determining that the call information indicates a rejection to answer;

a module configured to determine whether the call number is included in a predetermined set of numbers;

means configured to determine a classification corresponding to the call information as a first decline to answer classification in response to determining that the call number is included in the predetermined set of numbers; and

means configured to determine a classification corresponding to the call information as a second reject answer classification in response to determining that the call number is not included in the predetermined set of numbers; and is

The first determining module includes:

a module configured to increment the number of call information in the first decline answer category by 1 in response to determining that the call number is included in the predetermined set of numbers; and

means configured to increment the number of call information in the second reject answer category by 1 in response to determining that the call number is not included in the predetermined set of numbers.

12. The data processing apparatus according to any of claims 8-11, wherein the update module comprises at least one of:

means configured to update the configuration information for display based on the determined at least one of the number of configuration information and the number of call information corresponding to each category;

a module configured to update a reply policy associated with the intelligent voice conversation based on the determined at least one of the amount of configuration information and the amount of call information corresponding to each category; and

a module configured to update the first preset rule based on the determined at least one of the number of the configuration information and the number of the call information corresponding to each classification.

13. The data processing apparatus according to any one of claims 8-11, further comprising:

a second acquisition module configured to acquire time information associated with accessing the intelligent voice dialog application;

the second classification module is configured to classify the time information according to a second preset rule; and

a second determination module configured to determine a quantity of the time information corresponding to each classification.

14. The data processing apparatus according to claim 13, wherein the second preset rule comprises at least one of: a usage duration threshold and a usage frequency threshold.

15. An electronic device, comprising:

a memory, a processor, and a computer program stored on the memory,

wherein the processor is configured to execute the computer program to implement the steps of the method of any one of claims 1-7.

16. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of any of claims 1-7.

17. A computer program product comprising a computer program, wherein the computer program realizes the steps of the method of any one of claims 1-7 when executed by a processor.